CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering
📰 ArXiv cs.AI
Learn how CounterRefine improves factual question answering by retrieving counterevidence to refine answers and reduce errors
Action Steps
- Implement CounterRefine as a repair layer in your retrieval-grounded question answering model
- Use the model to produce a short answer from retrieved evidence
- Gather additional support and conflicting evidence with follow-up queries conditioned on the answer
- Refine the answer based on the retrieved counterevidence
- Evaluate the performance of the model with and without CounterRefine to measure its impact on accuracy
Who Needs to Know This
NLP engineers and researchers can benefit from this technique to improve the accuracy of their question answering models
Key Insight
💡 Retrieving counterevidence can help reduce errors in factual question answering by refining answers based on conflicting evidence
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🤖 Improve factual QA with CounterRefine, a lightweight repair layer that retrieves counterevidence to refine answers
Key Takeaways
Learn how CounterRefine improves factual question answering by retrieving counterevidence to refine answers and reduce errors
Full Article
Title: CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering
Abstract:
arXiv:2603.16091v2 Announce Type: replace-cross Abstract: In factual question answering, many errors are not failures of access but failures of commitment: the system retrieves relevant evidence, yet still settles on the wrong answer. We present CounterRefine, a lightweight inference-time repair layer for retrieval-grounded question answering. CounterRefine first produces a short answer from retrieved evidence, then gathers additional support and conflicting evidence with follow-up queries condi
Abstract:
arXiv:2603.16091v2 Announce Type: replace-cross Abstract: In factual question answering, many errors are not failures of access but failures of commitment: the system retrieves relevant evidence, yet still settles on the wrong answer. We present CounterRefine, a lightweight inference-time repair layer for retrieval-grounded question answering. CounterRefine first produces a short answer from retrieved evidence, then gathers additional support and conflicting evidence with follow-up queries condi
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